class_docstr: Builder for decentralized multiagent setup.
docstr: Returns simultaneous-acting multiagent actor instance.
  
  Args:
    random_key: random key.
    policy: dict of policies, one for each actor. Policies can
      be heterogeneous (i.e., distinct in architecture) across actors.
    environment_spec: the (multiagent) environment spec, which will be
      factorized into single-agent specs for replay table initialization.
    variable_source: an optional LearnerSet. Each sub_actor pulls its local
      variables from variable_source.
    adder: how data is recorded (e.g., added to replay) for each actor.
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code: def make_actor(  # Internal pytype check.
      self,
      random_key: networks_lib.PRNGKey,
      policy: ma_types.MultiAgentPolicyNetworks,
      environment_spec: specs.EnvironmentSpec,
      variable_source: Optional[core.VariableSource] = None,
      adder: Optional[Mapping[ma_types.AgentID, adders.Adder]] = None,
  ) -> core.Actor:
      """Returns simultaneous-acting multiagent actor instance.
  
      Args:
        random_key: random key.
        policy: dict of policies, one for each actor. Policies can
          be heterogeneous (i.e., distinct in architecture) across actors.
        environment_spec: the (multiagent) environment spec, which will be
          factorized into single-agent specs for replay table initialization.
        variable_source: an optional LearnerSet. Each sub_actor pulls its local
          variables from variable_source.
        adder: how data is recorded (e.g., added to replay) for each actor.
      """
      if adder is None:
          adder = {agent_id: None for agent_id in policy.keys()}
  
      sub_actors = {}
      for agent_id, builder in self._builders.items():
          single_agent_spec = ma_utils.get_agent_spec(
              environment_spec, agent_id
          )
          random_key, actor_key = jax.random.split(random_key)
          # Adds a prefix to each sub-actor's variable names to ensure the correct
          # sub-learner is queried for variables.
          sub_variable_source = PrefixedVariableSource(
              variable_source, f"{agent_id}{VARIABLE_SEPARATOR}"
          )
          sub_actors[agent_id] = builder.make_actor(
              actor_key,
              policy[agent_id],
              single_agent_spec,
              sub_variable_source,
              adder[agent_id],
          )
      return actor.SimultaneousActingMultiAgentActor(sub_actors)
  
imports: from typing import Dict, Iterator, List, Mapping, Optional, Sequence
  from acme import adders
  from acme import core
  from acme import specs
  from acme import types
  from acme.agents.jax import builders
  from acme.agents.jax.multiagent.decentralized import actor
  from acme.agents.jax.multiagent.decentralized import factories
  from acme.agents.jax.multiagent.decentralized import learner_set
  from acme.jax import networks
  from acme.multiagent import types
  from acme.multiagent import utils
  from acme.utils import counting
  from acme.utils import iterator_utils
  from acme.utils import loggers
  import jax
  import reverb
label_desc: Use image node_id 6 for calling the DecentralizedMultiAgentBuilder obj's underlying member method code with example usage: obj.make_actor(random_key, policy, environment_spec, variable_source, adder) and returns: actor